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  • Open Access

    ARTICLE

    A Comprehensive Investigation of Machine Learning Feature Extraction and Classification Methods for Automated Diagnosis of COVID-19 Based on X-ray Images

    Mazin Abed Mohammed1, Karrar Hameed Abdulkareem2, Begonya Garcia-Zapirain3, Salama A. Mostafa4, Mashael S. Maashi5, Alaa S. Al-Waisy1, Mohammed Ahmed Subhi6, Ammar Awad Mutlag7, Dac-Nhuong Le8,9,*

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3289-3310, 2021, DOI:10.32604/cmc.2021.012874

    Abstract The quick spread of the Coronavirus Disease (COVID-19) infection around the world considered a real danger for global health. The biological structure and symptoms of COVID-19 are similar to other viral chest maladies, which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease. In this study, an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods (e.g., artificial neural network (ANN), support vector machine (SVM), linear kernel and radial basis function (RBF), k-nearest neighbor… More >

  • Open Access

    ARTICLE

    An Optimal Deep Learning Based Computer-Aided Diagnosis System for Diabetic Retinopathy

    Phong Thanh Nguyen1, Vy Dang Bich Huynh2, Khoa Dang Vo1, Phuong Thanh Phan1, Eunmok Yang3,*, Gyanendra Prasad Joshi4

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2815-2830, 2021, DOI:10.32604/cmc.2021.012315

    Abstract Diabetic Retinopathy (DR) is a significant blinding disease that poses serious threat to human vision rapidly. Classification and severity grading of DR are difficult processes to accomplish. Traditionally, it depends on ophthalmoscopically-visible symptoms of growing severity, which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity. This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization (OPSO) algorithm-based Convolutional Neural Network (CNN) Model EOPSO-CNN in order to perform DR detection and grading. The proposed EOPSO-CNN model involves three main processes such as preprocessing, feature extraction, and classification. The proposed model initially involves… More >

  • Open Access

    REVIEW

    Progresses of mycobacteriophage-based Mycobacterium tuberculosis detection

    ZICHEN LIU1,#, SIYAO GUO2,3,#, MENGZHI JI1, KAILI SUN1, ZHONGFANG LI4,*, XIANGYU FAN1,*

    BIOCELL, Vol.44, No.4, pp. 683-694, 2020, DOI:10.32604/biocell.2020.011713

    Abstract Tuberculosis (TB) remains a major cause of morbidity and mortality worldwide, particularly in developing countries. A rapid and efficient method for TB diagnosis is indispensable to check the trend of tuberculosis expansion. The emergence of drug-resistant bacteria has increased the challenge of rapid drug resistance tests. Due to its high specificity and sensitivity, bacteriophage-based diagnosis is intensively pursued. In this review, we mainly described mycobacteriophage-based diagnosis in TB detection, especially two prevalent approaches: fluorescent reporter phage and phage amplified biologically assay (PhaB). The rationale of reporter phage is that phage carrying fluorescent genes can infect host bacteria specifically. Phage amplified… More >

  • Open Access

    ARTICLE

    Decreased CD10-positive granulocytes for the differential diagnosis of myelodysplastic syndrome

    JIYU WANG#, HUIPING WANG#, YING PAN, QIANSHAN TAO, ZHIMIN ZHAI*

    BIOCELL, Vol.44, No.4, pp. 607-611, 2020, DOI:10.32604/biocell.2020.010947

    Abstract Myelodysplastic syndromes (MDS) are highly heterogeneous myeloid neoplasms, and a large number of patients are difficult to diagnose and classify by blood and bone marrow examination. As a surface marker of granulocyte, studies have shown CD10 can be used to define the degree of granulocyte maturation in MDS patients. However, whether it can be used for differential diagnosis of MDS and other hematological diseases remains inconclusive. To explore the value of CD10 for differential diagnosis of MDS, 60 newly diagnosed MDS, 20 aplastic anemia (AA) patients, and 35 iron-deficient anemia (IDA) patients were selected for this study. Bone marrow (BM)… More >

  • Open Access

    ARTICLE

    LncRNA-ATB Can Be a Biomarker for Diagnosis and Prognosis Evaluation of Non-Small Cell Lung Cancer

    Nan Geng1, Wenxia Hu1, Zhikun Liu2, Jingwei Su2, Wenyu Sun3, Shaonan Xie4, Cuimin Ding1,*

    Oncologie, Vol.22, No.4, pp. 245-254, 2020, DOI:10.32604/oncologie.2020.014125

    Abstract Objective: This study was set out to inquire into the expression and clinical significance of lncRNA activated by transforming growth factor β (LncRNA-ATB) and in cancer tissues of patients with non-small cell lung cancer (NSCLC). Methods: LncRNA-ATB in cancer tissues and adjacent tissues of 89 NSCLC patients was detected by quantitative real-time polymerase chain reaction (qRT-PCR), and its clinical diagnostic value in NSCLC was determined by receiver operating characteristic (ROC) curves. Based on the median expression of LncRNAATB in NSCLC tissues, 89 patients were allocated into high- and low-expression groups. The 3-year survival rate was calculated using Kaplan-Meier method and… More >

  • Open Access

    ARTICLE

    A Hybrid Approach for the Lung(s) Nodule Detection Using the Deformable Model and Distance Transform

    Ayyaz Hussain1, Mohammed Alawairdhi2, Fayez Alazemi3, Sajid Ali Khan4, Muhammad Ramzan2,*

    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 857-871, 2020, DOI:10.32604/iasc.2020.010120

    Abstract The Computer Aided Diagnosis (CAD) systems are gaining more recognition and being used as an aid by clinicians for detection and interpretation of diseases every passing day due to their increasing accuracy and reliability. The lung(s) nodule detection is a very crucial and difficult step for CAD systems. In this paper, a hybrid approach for the lung nodule detection using a deformable model and distance transform has been proposed. The proposed method has the ability to detect all major kinds of nodules such as the juxta-plueral, isolated, and the juxta-vescular, along with the non-solid nodules automatically and intelligently. Results show… More >

  • Open Access

    ARTICLE

    Intelligent Prediction Approach for Diabetic Retinopathy Using Deep Learning Based Convolutional Neural Networks Algorithm by Means of Retina Photographs

    G. Arun Sampaul Thomas1, Y. Harold Robinson2, E. Golden Julie3, Vimal Shanmuganathan4, Seungmin Rho5, Yunyoung Nam6,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1613-1629, 2021, DOI:10.32604/cmc.2020.013443

    Abstract Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed, leak fluid and vision impairment. Symptoms of retinopathy are blurred vision, changes in color perception, red spots, and eye pain and it cannot be detected with a naked eye. In this paper, a new methodology based on Convolutional Neural Networks (CNN) is developed and proposed to intelligent retinopathy prediction and give a decision about the presence of retinopathy with automatic diabetic retinopathy screening with accurate diagnoses. The CNN model is trained by different images of eyes that have retinopathy and those which do… More >

  • Open Access

    ARTICLE

    An IoT-Cloud Based Intelligent Computer-Aided Diagnosis of Diabetic Retinopathy Stage Classification Using Deep Learning Approach

    K. Shankar1,*, Eswaran Perumal1, Mohamed Elhoseny2, Phong Thanh Nguyen3

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1665-1680, 2021, DOI:10.32604/cmc.2020.013251

    Abstract Diabetic retinopathy (DR) is a disease with an increasing prevalence and the major reason for blindness among working-age population. The possibility of severe vision loss can be extensively reduced by timely diagnosis and treatment. An automated screening for DR has been identified as an effective method for early DR detection, which can decrease the workload associated to manual grading as well as save diagnosis costs and time. Several studies have been carried out to develop automated detection and classification models for DR. This paper presents a new IoT and cloud-based deep learning for healthcare diagnosis of Diabetic Retinopathy (DR). The… More >

  • Open Access

    ARTICLE

    Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

    Gopi Krishna Durbhaka1, Barani Selvaraj1, Mamta Mittal2, Tanzila Saba3,*, Amjad Rehman3, Lalit Mohan Goyal4

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2041-2059, 2021, DOI:10.32604/cmc.2020.013131

    Abstract Nowadays, renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs. Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task. Condition monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches, practices and technology during the last decade. Turbines mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the defect. This paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the… More >

  • Open Access

    ARTICLE

    Breast Cancer: Delays of Access to Diagnosis and Treatment Retrospective Study–Batna, Algeria August 2015–February 2016
    Cancer du Sein: Délais d’Accès au Diagnostic et aux Traitements Etude Rétrospective–Batna, Algérie Août 2015–Février 2016

    Fadhila Mansour1,2,*, Abdelhak Lakehal2,3, Lahcène Nezzal2,3

    Oncologie, Vol.22, No.3, pp. 117-128, 2020, DOI:10.32604/oncologie.2020.014979

    Abstract The objective of this study was to quantify the delays in access to diagnosis and treatment of women with breast cancer who are managed at the anticancer center of Batna, Algeria. This was a descriptive retrospective study conducted during the period August 2015 to February 2016. In order to trace the history of the journey from the first signs of cancer to the first treatments, a questionnaire was filled in from the medical files and completed by an interview with the patients. A total of 267 patients were included in the study. The median time to management was 4.5 months.… More >

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